Automatic vs. manual feature engineering for anomaly detection of drinking-water quality

Abstract

This paper evaluates anomaly detection approaches for drinking-water quality. Two major machine learning techniques are compared. One is manual feature engineering with feature subset selection for dimensionality reduction. The other is automatic feature learning through a recurrent neural network. Both methods incorporate the time domain for change detection. Preliminary results show a superior performance of automatic feature learning with an F1 score of 80%. While the feature set proposed in this work out-performs naive classification with original features, it needs further analysis to reach comparable performance to the automatic approach.